CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning

Building virtual cells with generative models to simulate cel-lular behavior in silico is emerging as a promising paradigm for acceler-ating drug discovery. However, prior image-based generative approachescan produce implausible cell images that violate basic physical and bio-logical constraints. To address this, we propose to post-train virtual cellmodels with reinforcement learning (RL), leveraging biologically mean-ingful evaluators as reward functions. We design seven rewards spanningthree categories—biological function, structural validity, and morpho-logical correctness—and optimize the state-of-the-art CellFlux modelto yield CellFluxRL. CellFluxRL consistently improves over CellFluxacross all rewards, with further performance boosts from test-time scal-ing. Overall, our results present a virtual cell modeling framework thatenforces physically-based constraints through RL, advancing beyond “vi-sually realistic” generations towards “biologically meaningful” ones.